As more devices use cellular networks, and the amount of data/applications desired for use by users in the cellular networks increases, it will become more challenging for network operators to meet the demand of their users. One technology which has been seen to be a way to boost the link capacity is the Multiple Input Multiple Output (MIMO) technology. Similarly, a newer technology known as Coordinated Multi-Point (CoMP) systems can be seen as a way to boost the capacity of cellular networks.
Each cell of a CoMP system, a so-called “CoMP-cell” or, in a more general context, also known as a “supercell”, is typically composed of several geographically distributed Transmission Points (TPs) connected through a fast backhaul to a Central Processing Unit (CPU). Under adequate processing circumstances, multiple TPs can simultaneously service multiple User Equipments (UEs). However, the coordination complexity in CoMP systems grows with the size of the set of TPs to be coordinated. The network infrastructure bears the cost of increased overhead in several facets: UE's feedback, backhaul traffic, and central processing.
In order to reduce complexity, coordination can be restricted to occur just within subsets or clusters of TPs. The cluster size is the common factor affecting all the aforementioned overhead facets as described in the article “The Value of Dynamic Clustering of Base Stations for Future Wireless Networks” by Papadogiannis and Alexandropoulos found in IEEE International Conference on Fuzzy Systems (FUZZ), 2010, pp. 1-6. The set of all available TPs belonging to a CoMP-cell can be partitioned into clusters, i.e., mutually exclusive subsets of TPs. Thus, each cluster can be seen as a distributed antenna array, which services the UEs associated with it.
Traditionally, the approaches for forming the TP clusters are classified into static and dynamic, depending on how frequently the clusters' composition is updated, however, semi-dynamic approaches are known as well. Examples of static approaches can be found in, the article “Limited Downlink Network Coordination in Cellular Networks” by Boccardi and Huang found in Proc. IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC), September 2007, pp. 1-5. Examples of dynamic approaches can be found in the article “The Value of Dynamic Clustering of Base Stations for Future Wireless Networks” by Papadogiannis and Alexandropoulos found in IEEE International Conference on Fuzzy Systems (FUZZ), 2010, pp. 1-6, and in the article “A Dynamic Clustering Approach in Wireless Networks with Multi-cell Cooperative Processing” by Papadogiannis et al. found in Proc. IEEE International Conference on Communications (ICC), May 2008, pp. 4033-4037. Examples of semi-dynamic approaches can be found in the article “Clustering Approach in Coordinated Multi-Point Transmission/Reception System” by Huang et al. found in Vehicular Technology Conference Fall (VTC 2010-Fall), 2010 IEEE 72nd, 2010, pp. 1-5.
Static clustering is the simplest approach, which requires fixed grouping of TPs, based for example on the TPs' positions and antenna radiation patterns. For semi-dynamic clustering, the clusters are fixed as well, although multiple layers of these configurations are provided, so that a UE at the cell's edge in a layer is at the cell's center in another layer. In the dynamic clustering approach, the form of the clusters can be adapted to current channel and load conditions, thus allowing a radio communication system to better exploit the available macroscopic spatial diversity. However, each of these approaches as currently described have their own challenges, some of which are described below.
The static clustering approach has its performance penalized for not adapting the cluster form to temporal variations of the traffic loads. Semi-dynamic clustering approaches, like that described in the article “Clustering Approach in Coordinated Multi-Point Transmission/Reception System” by Huang et al. found in Vehicular Technology Conference Fall (VTC 2010-Fall), 2010 IEEE 72nd, 2010, pp. 1-5, attempt to overcome this limitation, albeit partially. For example, one semi-dynamic approach requires prior planning of the layers, tightly related to the TP deployment, as well as to foreseen demands for coordination. However, in any other situation the coordination within the cluster may not be efficient or it may even be harmful to other transmissions.
Dynamic approaches seem to be the most suitable ones for adapting the cluster forms to the many different changes on the channel and traffic conditions, whether temporal or spatial. Nonetheless, the algorithms published so far, e.g., in the references described above, are not fully adequate since the algorithms restrict the size of the clusters and they demand substantial computational processing.
The process of cluster formation involves TP selection and is tightly related to UE scheduling and coordinated transmission. The so-called “greedy algorithm” described in the article “A Dynamic Clustering Approach in Wireless Networks with Multi-cell Cooperative Processing” by Papadogiannis et al. found in Proc. IEEE International Conference on Communications (ICC), May 2008, pp. 4033-4037, depends on a broad and sequential combination of candidate TPs, favoring the clusters formed earlier. Furthermore, at each combination tested by the greedy algorithm, Zero-Forcing (ZF) precoders have to be computed in order to determine the sum-rate, therefore involving a number of matrix inversion operations, whose complexity scales up cubically with the cluster size. Alternatively, and unlike this maximum sum-rate approach, the algorithm proposed in the article “The Value of Dynamic Clustering of Base Stations for Future Wireless Networks” by Papadogiannis and Alexandropoulos found in IEEE International Conference on Fuzzy Systems (FUZZ), 2010, pp. 1-6, is based on a long-term Channel State Information (CSI) and requires an exhaustive search over all possible cluster formations.
Accordingly, more efficient systems and methods for use in CoMP systems are desirable.